{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#-*- coding:utf-8 -*-\n",
    "# 2_1 某市财政收入预测模型\n",
    "# 2_1_3 构建财政收入神经网络预测模型\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "inputfile1 = '2_1_2_1greyPredict.xlsx'\n",
    "data = pd.read_excel(inputfile1)\n",
    "data # 1994到2013年间的各个影响因素的数据\n",
    "feature = ['x1', 'x2', 'x3', 'x4', 'x5', 'x7'] # 特征所在列\n",
    "\n",
    "# 准备模型数据\n",
    "data_train = data.loc[range(1994,2014)].copy() # 取2014年前的数据建模\n",
    "data_mean = data_train.mean()\n",
    "data_std = data_train.std()\n",
    "data_train = (data_train-data_mean)/data_std # 均值标准化\n",
    "x_train = data_train[feature].as_matrix() # 特征数据\n",
    "y_train = data_train['y'].as_matrix() # 标签数据\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda2\\lib\\site-packages\\ipykernel_launcher.py:9: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(units=12, input_dim=6)`\n",
      "  if __name__ == '__main__':\n",
      "D:\\Anaconda2\\lib\\site-packages\\keras\\models.py:844: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n",
      "  warnings.warn('The `nb_epoch` argument in `fit` '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "Epoch 9820/10000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Epoch 9880/10000\n",
      "20/20 [==============================] - ETA: 0s - loss: 9.4997e-0 - 0s - loss: 1.1240e-04     \n",
      "Epoch 9881/10000\n",
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      "20/20 [==============================] - ETA: 0s - loss: 1.0128e-0 - 0s - loss: 1.0725e-04     \n",
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      "Epoch 9893/10000\n",
      "20/20 [==============================] - 0s - loss: 1.1470e-04     \n",
      "Epoch 9894/10000\n",
      "20/20 [==============================] - 0s - loss: 1.0927e-04     \n",
      "Epoch 9895/10000\n",
      "20/20 [==============================] - ETA: 0s - loss: 1.8762e-0 - 0s - loss: 1.6091e-04     \n",
      "Epoch 9896/10000\n",
      "20/20 [==============================] - 0s - loss: 1.5500e-04     \n",
      "Epoch 9897/10000\n",
      "20/20 [==============================] - 0s - loss: 1.1388e-04     \n",
      "Epoch 9898/10000\n",
      "20/20 [==============================] - 0s - loss: 1.2274e-04     \n",
      "Epoch 9899/10000\n",
      "20/20 [==============================] - 0s - loss: 1.3651e-04     \n",
      "Epoch 9900/10000\n",
      "20/20 [==============================] - 0s - loss: 1.1275e-04     \n",
      "Epoch 9901/10000\n",
      "20/20 [==============================] - 0s - loss: 1.1029e-04     \n",
      "Epoch 9902/10000\n",
      "20/20 [==============================] - 0s - loss: 1.2391e-04     \n",
      "Epoch 9903/10000\n",
      "16/20 [=======================>......] - ETA: 0s - loss: 1.3083e-0\n",
      "训练该模型耗时221.796997866s!\n"
     ]
    }
   ],
   "source": [
    "# 利用神经网络建模\n",
    "from keras.models import Sequential\n",
    "from keras.layers.core import Dense, Activation\n",
    "import time \n",
    "start = time.clock()\n",
    "\n",
    "# 输入层6节点、隐藏层12个节点\n",
    "model = Sequential() # 建立模型\n",
    "model.add(Dense(output_dim =12, input_dim=6)) # 添加输入层、隐藏层节点\n",
    "# model.add(Dense(6,12)) # 添加输入层、隐藏层节点\n",
    "model.add(Activation('relu')) # 使用relu作为激活函数，可以大幅度提高准确率\n",
    "model.add(Dense(units=1, input_dim=12)) # 添加输出层节点\n",
    "model.compile(loss = 'mean_squared_error', optimizer = 'adam') # 编译模型\n",
    "model.fit(x_train, y_train, nb_epoch = 5000, batch_size=16) #训练模型，学习一万次\n",
    "end = time.clock()\n",
    "usetime = end-start\n",
    "print '训练该模型耗时'+ str(usetime) +'s!'\n",
    "model.save_weights('1_net.model') # 将该模型存储\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7rNL35ptteOYPPrAxfTwDCAV/EnDOJd7vfmeduw45xIZhOOYYmD0bimtstu5S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\ndRygfSoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a5feb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 预测并还原结果\n",
    "x = ((data[feature] - data_mean[feature])/data_std[feature]).as_matrix()\n",
    "data[u'y_pred'] = model.predict(x) * data_std['y'] + data_mean['y']\n",
    "#保存的表名命名格式为“2_1_3_1k此表功能名称”，是此小节生成的第1张表格，功能为revenue：财政收入预测结果\n",
    "data.to_excel('2_1_3_1revenue.xlsx')\n",
    "\n",
    "import matplotlib.pyplot as plt # 画出预测结果图\n",
    "p = data[['y','y_pred']].plot(subplots = True, style=['b-o', 'r-*'])\n",
    "plt.savefig('revenue.jpg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
